Call processing method and apparatus, server, storage medium, and system

ABSTRACT

Example embodiments of this disclosure provide a method, an apparatus, a server, a storage medium, and a system for call processing. The method includes: monitoring, in real time, a call processing process of an artificial intelligence AI robot, to obtain an interaction text of the call, where the interaction text includes a recognition result of a user question and a reply to the user question; obtaining a service level value of the AI robot for the call based on the obtained interaction text; and when the service level value meets a first condition, performing an intervention operation on the call by using a target agent device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/CN2019/082667, filed on Apr. 15, 2019, which claims priority toChinese Patent Application No. 201810339746.3, filed on Apr. 16, 2018.The disclosures of the aforementioned applications are herebyincorporated by reference in their entireties.

TECHNICAL FIELD

Example embodiments of this disclosure relate to the communicationsfield, and in particular, to a method, an apparatus, a server, a storagemedium, and a system for call processing.

BACKGROUND

As an important bridge between an enterprise and a client, a call centerhandles incoming and outgoing calls through a plurality of channels suchas voice, a text, and a video, to provide good service experience for auser and play a key role in maintaining the client and improving aservice for the enterprise. In recent years, a breakthrough in anartificial intelligence (AI) technology has brought new opportunities tothe development of the call center. Introduction of the artificialintelligence technology enables an AI robot to assist people and replacepeople. This has become a development trend of a customer serviceindustry. However, due to the current levels of intelligent voice andnatural language processing technologies, the interaction capability ofa current AI robot cannot entirely replace that of a human agent in acomplex language environment and different service backgrounds.Therefore, the human agent usually needs to participate in a process ofprocessing a user service by the AI robot.

Currently, in the process of processing the user service by the AIrobot, if a user is not satisfied with a service of the AI robot, theuser may proactively request to transfer a call to a human agent forprocessing, and the human agent learns of the user's requirementsthrough a historical interaction record or re-communication, andcompletes handling of a subsequent service of the user.

In the process of processing the call by using the foregoing technology,the user needs to proactively request to transfer the call to the humanagent, and re-describe a service requirement to the human agent. Thehuman agent needs to spend time on understanding the service requirementand is responsible for handling the subsequent service of the user. Thecall occupies the human agent for a relatively long time, therebyaffecting working efficiency of the human agent and an overall servicelevel of a system. Consequently, call processing efficiency is low.

SUMMARY

Example embodiments of this disclosure provide a method, an apparatus, aserver, a storage medium, and a system for call processing, to resolve aproblem of low call processing efficiency in a related technology. Thetechnical solutions are as follows.

According to a first aspect, a call processing method is provided, andthe method includes:

monitoring, by a server in real time, a call processing process of anartificial intelligence AI robot, to obtain an interaction text of thecall, where the interaction text includes a recognition result of a userquestion and a reply to the user question;

obtaining, by the server, a service level value of the AI robot for thecall based on the interaction text; and

when the service level value meets a first preset condition, performing,by the server, an intervention operation on the call by using a targetagent device, where the target agent device is a device of a human agentthat assists the AI robot in call processing.

According to the method provided in this embodiment of this disclosure,a service level of the AI robot is evaluated, and when the service levelvalue meets the preset condition, the human agent is automaticallytriggered for intervention, and the human agent only assists in the callprocessing process of the AI robot. In this way, a problem that it takesan excessively long time for the human agent to directly undertake acall is resolved, so that working efficiency of the human agent and anoverall service level of a system are ensured, thereby improving callprocessing efficiency.

In a first possible implementation of the first aspect, the obtaining,by the server, a service level value of the AI robot for the call basedon the interaction text includes:

determining, by the server, at least one of AI complexity, servicecomplexity, or user complexity based on the interaction text, where theAI complexity is used to reflect service quality of the AI robot, theservice complexity is used to reflect a complexity degree of a service,and the user complexity is used to reflect a degree of a userrequirement for service handling; and

obtaining, by the server, the service level value based on the at leastone of the AI complexity, the service complexity, or the usercomplexity.

According to the method provided in this embodiment of this disclosure,the service level value of the AI robot is determined by using the atleast one of the AI complexity, the service complexity, or the usercomplexity. Because impact of factors such as the AI robot, the service,and the user are considered, accuracy of the determined service levelvalue is relatively high.

In a second possible implementation of the first aspect, the AIcomplexity is determined based on at least one of a quantity of questionrepetitions, a quantity of recognition failures, a questioning keyword,a user tone change, a maximum duration of question recognition, or amaximum length of a single reply;

the service complexity is determined based on at least one of a userconsultation duration, a quantity of rounds of consultation interaction,or a service level; and

the user complexity is determined based on a quantity of times ofrepeated dialing for a question.

In a third possible implementation of the first aspect, the methodfurther includes:

obtaining, by the server, an estimated service evaluation value based onthe service level value, a historical service level value, and ahistorical service evaluation value, where the historical service levelvalue is a service level value of any previous call of a same service,and the historical service evaluation value is a satisfaction evaluationvalue of any previous call of a same service; and

when the estimated service evaluation value is less than the historicalservice evaluation value or a preset expected service evaluation value,determining, by the server, that the service level value meets the firstpreset condition.

According to the method provided in this embodiment of this disclosure,an alarm mechanism for a service level is determined according to apolicy for ensuring a user service evaluation. Because a user evaluationmay reflect whether the user is satisfied with call processing by the AIrobot and a degree of satisfaction, an alarm for the service level canmeet a real intention of the user to some extent in this manner.

In a fourth possible implementation of the first aspect, the methodfurther includes:

determining, by the server, an overall service level value, where theoverall service level value is an average value of service level valuesof the AI robot for all calls of a same service in a preset period; and

when the service level value is less than the overall service levelvalue or a preset expected service level value, determining, by theserver, that the service level value meets the first preset condition.

According to the method provided in this embodiment of this disclosure,an alarm mechanism for a service level is determined according to apolicy for ensuring an overall service level. The overall service levelvalue can reflect an average level of processing calls of a same serviceby the AI robot. Therefore, an alarm for the service level can improvealarm accuracy to some extent in this manner.

In a fifth possible implementation of the first aspect, the performing,by the server, an intervention operation on the call by using a targetagent device includes:

sending, by the server, the interaction text to the target agent device,and obtaining a corrected question text and a corrected reply text thatare obtained after the target agent device corrects the interactiontext; or

transferring, by the server, the call to the target agent device forprocessing; or

establishing, by the server, a three-party conference connection amongthe target agent device, the AI robot, and user equipment, where theuser equipment is a device that initiates the call.

According to the method provided in this embodiment of this disclosure,the target agent device may perform any intervention operation such astext correction, call interception, or session interposition on thecall, thereby improving intervention effectiveness.

In a sixth possible implementation of the first aspect, the sending, bythe server, the interaction text to the target agent device, andobtaining a corrected question text and a corrected reply text that areobtained after the target agent device corrects the interaction textincludes:

sending, by the server, the interaction text to the target agent device;

sending, by the target agent device, the corrected question text to theserver;

sending, by the server, the corrected question text to the AI robot;

obtaining, by the AI robot, the corrected question text sent by thetarget agent device, where the corrected question text is a textobtained after a voice recognition result of the user question iscorrected;

obtaining, by the AI robot, the reply to the user question based on thecorrected question text;

obtaining, by the server, the corrected reply text sent by the targetagent device, where the corrected reply text is a text obtained afterthe reply to the user question is corrected; and

playing, by the server, the corrected reply text.

According to the method provided in this embodiment, the target agentdevice may correct the user question and the reply, thereby improving anoverall service level of a system. Therefore, call processing efficiencyis high.

In a seventh possible implementation of the first aspect, after theserver corrects the interaction text by using the target agent device,the method further includes:

when the service level value of the AI robot for the call meets a secondpreset condition, stopping, by the server, the intervention operation onthe call; or

after correcting the voice recognition result of the user question,stopping, by the server, the intervention operation on the call.

According to the method provided in this embodiment, the target agentdevice may quit the intervention at an appropriate occasion, therebyavoiding a problem that working efficiency is affected because anexcessively long time is occupied.

In an eighth possible implementation of the first aspect, before theperforming, by the server, an intervention operation on the call byusing a target agent device, the method further includes:

determining, by the server, the target agent device based on a servicecorresponding to the call and subscription information, where thesubscription information is used to record a service to which each agentdevice subscribes.

According to the method provided in this embodiment, a to-be-monitoredservice is subscribed to in advance, and therefore the system maydetermine the target agent device based on subscription information whena call is connected.

In a ninth possible implementation of the first aspect, after thedetermining, by the server, the target agent device based on a servicecorresponding to the call and subscription information, the methodfurther includes:

adding, by the server, the call to a monitoring queue of the targetagent device, where the monitoring queue is used to manage callinformation, a call monitoring status, and an interaction text of atleast one call, the call information includes a call number, callingparty information, and called party information, and the call monitoringstatus includes an idle state, a monitoring state, or an interventionstate.

According to the method provided in this embodiment, a call is added toa corresponding monitoring queue, thereby facilitating management of thecall by the target agent device.

In a tenth possible implementation of the first aspect, the first presetcondition is set based on a service type.

According to the method provided in this embodiment, a condition fortriggering intervention is set based on a service, and differentrequirements of different services for a service level value are fullyconsidered, thereby improving accuracy of triggering intervention.

According to a second aspect, a call processing apparatus is provided.The apparatus includes a plurality of function modules, and theplurality of function modules are configured to perform the callprocessing method provided in any one of the first aspect and thepossible implementations of the first aspect.

According to a third aspect, a server is provided, and the serverincludes a processor and a memory. The memory stores a computer program,and the computer program is loaded and executed by the processor toimplement the call processing method provided in any one of the firstaspect and the possible implementations of the first aspect.

According to a fourth aspect, a computer-readable storage medium isprovided. The computer-readable storage medium stores a computerprogram, and the computer program is loaded and executed by a processorto implement the call processing method provided in any one of the firstaspect and the possible implementations of the first aspect.

According to a fifth aspect, a call processing system is provided, andthe system includes a server, an AI robot and a target agent device. Theserver is configured to perform the call processing method provided inany one of the first aspect and the possible implementations of thefirst aspect.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic diagram of a system according to an embodiment ofthis disclosure;

FIG. 2 is a schematic diagram of a system according to an embodiment ofthis disclosure;

FIG. 3 is a schematic diagram of a server according to an embodiment ofthis disclosure;

FIG. 4 is a flowchart of a call processing method according to anembodiment of this disclosure;

FIG. 5 is a diagram illustrating a call processing process according toan embodiment of this disclosure;

FIG. 6 is a schematic diagram of one-to-many monitoring according to anembodiment of this disclosure;

FIG. 7 is a schematic diagram of an alarm scenario based on a serviceevaluation value according to an embodiment of this disclosure;

FIG. 8 is a schematic diagram of monitoring and evaluating a callaccording to an embodiment of this disclosure;

FIG. 9 is a diagram illustrating a call processing process according toan embodiment of this disclosure; and

FIG. 10 is a schematic diagram of a call processing apparatus accordingto an embodiment of this disclosure.

DESCRIPTION OF EMBODIMENTS

Before the embodiments of this disclosure are described in detail, somekey terms in the embodiments of this disclosure are first described.

Voice recognition: A voice recognition technology is a technology inwhich a voice signal of a person is converted into text information thatmay be recognized by a computer system, so that the computer system canunderstand a human language. In a traditional call center, voicerecognition is usually used to replace a button with a voice command. Inan intelligent customer service era, voice recognition is mainly used invoice navigation and human-machine voice interaction scenarios.

Voice synthesis: A voice synthesis technology is a technology in whichtext information is converted into voice and is played as voice, toautomatically convert a text into continuous human voice in real time.In a traditional call center, voice synthesis is usually used to playvoice that needs to change based on different service contents. In anintelligent customer service era, voice synthesis is mainly used invoice navigation and human-machine voice interaction scenarios.

Natural language processing: Natural language processing includesnatural semantic understanding and natural language generation. Naturalsemantic understanding is used to convert a human language into alabeled machine language, and natural language generation is used toconvert a machine language into a human language.

Computer telephony integration (CTI): CTI is a general term covering anyway of integrating a computer with a telephone system. CTI is mostcommonly used in a system in which an application program is used todisplay detailed information about an incoming or outgoing call.

AI robot: An AI robot replaces a human agent by using intelligent voiceand natural language processing technologies, thereby implementing aninterconnection between the AI robot and a third server.

Agent device: An agent device is a device of a human agent that assistsan AI robot in call processing. This type of agent device is mainly usedto assist the AI robot, to improve recognition of a user problem andcorrection of a reply of the AI robot. If necessary, the agent devicecan also answer a call and handle a service.

FIG. 1 is a schematic diagram of a system according to an embodiment ofthis disclosure. Referring to FIG. 1 the system includes a plurality ofservers, an AI robot, and a plurality of agent devices. The plurality ofservers may include a first server, a second server, a third server, anintelligent voice server, and a fourth server.

The first server provides capabilities such as unified multimedia accessand a flexible routing policy. In embodiments of this disclosure, thefirst server may be a CTI server. The first server takes a most criticalrole and is responsible for routing a call and evaluating a servicelevel. FIG. 2 is a schematic diagram of a system according to anembodiment of this disclosure. As shown in FIG. 2, a first server mayinclude a monitoring and detection module and an intervention operationmodule, and may further include a subscription management module and asilence rule configuration module. The subscription management module isconfigured to manage a correspondence between an agent device and aservice. The silence rule configuration module is adapted to configure arule for the agent device to assist an AI robot in call processing. Themonitoring and detection module is configured to: monitor, in real time,a call processing process of the AI robot, and detect and evaluate aservice level of the AI robot. The intervention operation module isconfigured to send an alarm to the agent device to trigger the agentdevice to perform an intervention operation on a call.

It should be noted that in this embodiment, an example in whichfunctions of modules such as the subscription management module, thesilence rule configuration module, the monitoring and detection module,and the intervention operation module are integrated into the firstserver is used for description. Actually, the foregoing modules mayalternatively form an independent device. Physical implementations ofthe modules are not limited in embodiments of this disclosure, providedthat the functions of the modules can be implemented.

A second server is responsible for unified session management, andprovides internet protocol (IP) voice call access, phone numberregistration and media resource processing functions. In someembodiments, the second server may be a unified session management (USM)server, and the second server is responsible for a media operation of acall, and implements an interconnection with an intelligent voiceserver.

A third server is responsible for parsing and executing a procedure fileloaded onto the third server. This type of file may complete a specifiedfunction such as an automatic voice prompt or user informationcollection. In embodiments of this disclosure, the third server may bean interactive voice response (IVR) server. The third server isresponsible for service logic and procedure processing, and implementsan interconnection with the AI robot.

The intelligent voice server includes a voice recognition (AutomaticVoice Recognition, ASR) module and a voice synthesis (Text To Voice,TTS) module, and provides automatic voice recognition and text-to-voicecapabilities. The ASR module is responsible for recognition of commonvoice, and the TTS module is responsible for text-to-voice synthesis.

The AI robot includes a natural language processing (NLP) module, andhas natural language recognition, problem checking and natural languagegeneration capabilities.

A fourth server provides an agent access capability. In embodiments ofthis disclosure, the fourth server may be an agent server, and thefourth server is responsible for access and management of a plurality ofagent devices. The plurality of agent devices are devices of humanagents that assist the AI robot in call processing.

It should be noted that this embodiment of this disclosure is describedby using an example in which the system provides call access by usingthe second server. Actually, a call access function may be alternativelyprovided by another device. For example, the system may further includea trunk gateway, and the trunk gateway provides narrowband relay access,to access a call initiated by a user on an operator side.

It should be noted that this embodiment of the present invention isdescribed by using an example in which the system includes a pluralityof servers that are the first server, the second server, the thirdserver, the intelligent voice server, and the fourth server. Actually,the system may include only one server, provided that the server canimplement functions provided by the foregoing servers.

FIG. 3 is a schematic diagram of a server 300 according to an embodimentof this disclosure. The server 300 may be provided as each server inFIG. 1. Referring to FIG. 3, the server 300 may include a processor 310and a memory 320. The memory 320 stores a computer program, and theprocessor 310 is configured to execute the computer program stored inthe memory 320, to perform the call processing method in the followingembodiments. The processor 310 receives a command from another element,decrypts the received command, and performs calculation or processesdata according to the decrypted command. The memory 320 may include aprogram module, for example, a kernel, middleware, an applicationprogramming interface (API), and an application. The program module mayinclude software, firmware, hardware, or at least two of the software,the firmware, and the hardware.

In an example embodiment, a computer-readable storage medium is furtherprovided, such as a memory that stores a computer program, and thecomputer program may be loaded and executed by a processor to completethe call processing method in the following embodiments. For example,the computer-readable storage medium may be a read-only memory (ROM), arandom access memory (RAM), a compact disc read-only memory (CD-ROM), atape, a floppy disk, or an optical data storage device.

FIG. 4 is a flowchart of a call processing method according to anembodiment of this disclosure. The method may be applied to the callprocessing system shown in FIG. 1. Referring to FIG. 4, the callprocessing method includes the following steps.

401. A first server obtains a silence rule, where the silence rule is arule for an agent device to assist an AI robot in call processing.

In this embodiment, an administrator may configure the silence rule byusing the first server based on a service type, and different servicesmay correspond to different silence rules. The silence rule may includean alarm threshold, an intervention operation (namely, a processingmanner triggered by an alarm), and a quantity of concurrently monitoredcalls.

The alarm threshold may include an expected service level value and anexpected service evaluation value. The expected service level value andthe expected service evaluation value may be set based on a servicetype. In other words, different expected service level values andexpected service evaluation values are set for different types ofservices. The intervention operation may include text correction, callinterception, and/or interposition. The text correction is performed onan interaction text of a call, and the interaction text is a textobtained by a system in a process of interaction between an AI robot anda user, and includes a recognition result of a user question and a replyto the user question. The call interception is transferring the call toa device of a human agent other than the AI robot for processing. Theinterposition is that the system establishes a three-party conferenceconnection among the device of the human agent, the AI robot, and userequipment. The user equipment is a device that initiates the call. Thehuman agent can hear interaction between the user and the AI robot, andmay replace the AI robot to answer the user question if necessary. Thequantity of concurrently monitored calls is a quantity of calls that aresimultaneously monitored by a single agent device.

It should be noted that step 401 may be actually performed by a silencerule configuration module of the first server.

402. The first server obtains subscription information, where thesubscription information is used to record a service to which each agentdevice subscribes.

The service is a service that may be processed by the AI robot, such asbroadband service handling, a user historical record query, or a packagechange.

In this embodiment of this disclosure, each agent device may log in tothe system (namely, access the system) by using a fourth server. Afterlogging in to the system, the agent device may set, by using the firstserver, a service that needs to be monitored. For example, a systemadministrator may set, based on a service skill of the human agent, aservice that needs to be monitored by the agent device. In this way, thefirst server may generate subscription information of each agent devicebased on a service that is set for each agent device. In addition, thesystem administrator may further set a corresponding silence rule basedon a service to which the agent device subscribes.

Further, the agent device may request to monitor a call of a specifictype of service. For example, the agent device may send a subscriptionmanagement request to the first server, and the first server may createa corresponding monitoring and detection task according to thesubscription management request. The subscription management request maycarry the service to which the agent device subscribes and thecorresponding silence rule.

It should be noted that step 402 may be actually performed by asubscription management module of the first server.

403. The AI robot processes a call after a call connection isestablished between the AI robot and the user equipment.

In this embodiment of this disclosure, if the user needs to consult aservice or requests handling of a service, the user may proactively makea call to a system of a call center (namely, the system shown in FIG.1), to establish a call connection to the call center. FIG. 5 is adiagram illustrating a call processing process according to anembodiment of this disclosure. As shown by a procedure indication line 1in FIG. 5, in a possible implementation, a process of establishing acall connection includes: a call initiated by a user is connected to atrunk gateway of a call center from an operator by using a relay such asE1/T1; the trunk gateway converts a data relay signaling protocol suchas primary rate access (PRA) or a primary rate interface (PRI) into abroadband session initiation protocol (SIP) signaling protocol; a secondserver establishes a call, and requests a route from a first server; thefirst server routes the call to a third server based on called partyinformation and user information; and the first server establishes acall connection between user equipment and the call center.

A service procedure of the third server implements interconnection withan AI robot. A service procedure in which the first server routes thecall to the third server is to route the call to the AI robot.Correspondingly, establishing the call connection between the userequipment and the call center is to establish a call connection betweenthe user equipment and the AI robot, to implement a dialog between theuser and the AI robot. For example, the user may speak a to-be-consultedquestion, and the AI robot may reply to the user question. It should benoted that, after the call connection is established, the first servermay determine a target agent device configured to monitor a callprocessing process. In a possible implementation, the first serverdetermines the target agent device based on a service corresponding tothe call and subscription information. The target agent device is anagent device that subscribes to the service corresponding to the call,and the subscription information is used to record a service to whicheach agent device subscribes.

After determining the target agent device, the first server may allocatethe call to the target agent device, and the target agent devicemonitors an interaction text in the call processing process. Amonitoring process includes browsing the interaction text. In a possibleimplementation, that the first server allocates the call to the targetagent device includes: the first server may add the call to a monitoringqueue of the target agent device. The monitoring queue is used to managecall information, a call monitoring status, and event information of atleast one call, the call information includes a call number (CALLID),calling party information, and called party information, and the callmonitoring status includes an idle state, a monitoring state, or anintervention state. The event information includes the interaction textand additional information fed back by the AI robot, for example, a useremotion change. FIG. 6 is a schematic diagram of one-to-many monitoringaccording to an embodiment of this disclosure. As shown in FIG. 6, an AIrobot 1 processes a call of a user 1, an AI robot 2 processes a call ofa user 2, and one agent device may simultaneously monitor callprocessing processes of a plurality of users (the user 1 and the user2).

It should be noted that this manner is described by using an example inwhich when a call is connected, the system allocates the call to thetarget agent device and adds the call to the monitoring queue of thetarget agent device. Actually, when the target agent device logs in tothe system to subscribe to a service, the system may allocate, to thetarget agent device, a call that is being processed by the AI robot, andadd the allocated call to the monitoring queue. In this way, themonitoring queue includes not only a current newly-connected call butalso call(s) that is being processed by the AI robot.

404. The first server monitors, in real time, a call processing processof the AI robot, to obtain an interaction text of the call, where theinteraction text includes a recognition result of a user question and areply to the user question.

In this embodiment of this disclosure, after the call of the user isconnected to the call center, the user may speak a service that needs tobe consulted, and interact with the AI robot. As shown by a procedureindication line 2 in FIG. 5, in a possible implementation, the user andthe AI robot may perform voice interaction. A specific interactionprocess may include: the second server interconnects with an intelligentvoice server by using a media resource control protocol (MRCP), andtransfers a voice stream to the intelligent voice server; theintelligent voice server recognizes the voice stream, and returns arecognition result to the second server; the second server reports therecognition result to the first server through a call channel; the firstserver routes the call to the specified third server; the third serverperforms corresponding service logic processing such as a userinformation query based on the service procedure; the third serverobtains the recognition result from the first server, and forwards therecognition result to an NLP module of the AI robot for processing; theNLP module returns a natural language text generated after analysisprocessing to the third server; the third server requests, by using thefirst server and the second server, to play the language text; and thesecond server requests the intelligent voice server to perform voicesynthesis, and the second server plays synthesized voice to the user.

The voice stream is a voice data stream including a user question, therecognition result is a recognition result of the voice data stream, andthe natural language text generated after the analysis processing is areply of the AI robot to the user question. In a process in which the AIrobot processes the call of the user, the first server may monitor theprocessing process of the AI robot to obtain an interaction text betweenthe AI robot and the user. Specifically, the first server may monitorthe interaction text of the call based on a subscription status of anagent device, a current status of an agent, and a configured silencerule.

It should be noted that, in the foregoing manner, the recognition resultis reported by the second server to the first server. Actually, therecognition result may be directly obtained by the first server from theAI robot. This is not limited in embodiments of this disclosure. Theforegoing manner is described by using a voice call as an example.Actually, for a text call or another media call, the first server maysimilarly obtain an interaction text in a call processing process of theAI robot. However, during the text call, voice recognition does not needto be performed on a user question, and voice synthesis does not need tobe performed on a reply to a question either.

In a possible implementation, the first server may synchronously pushthe interaction text in the call processing process of the AI robot tothe target agent device, so that the target agent device cansynchronously monitor the interaction text in the call processingprocess.

It should be noted that step 404 may be actually performed by amonitoring and detection module of the first server.

405. The first server obtains, based on the interaction text, a servicelevel value of the AI robot for the call.

In a possible implementation, that the first server obtains, based onthe interaction text, a service level value of the AI robot for the callincludes the following steps 405A and 405B.

405A. Determine at least one of AI complexity, service complexity, oruser complexity based on the interaction text, where the AI complexityis used to reflect service quality of the AI robot, the servicecomplexity is used to reflect a complexity degree of a service, and theuser complexity is used to reflect a degree of a user requirement forservice handling.

Because the interaction text may reflect a status of interaction betweenthe AI robot and the user, the first server may determine the servicequality of the AI robot based on the interaction text. In a possibleimplementation, for the AI complexity, the first server may determine,based on the interaction text, an average duration of answering userquestions by the AI robot. The first server may learn, from theinteraction text, a time point at which the user asks each question anda time point at which the AI robot provides each reply, determine, basedon the two time points, a duration of answering each user question bythe AI robot, and obtain the AI complexity based on an average value ofthe duration. For the service complexity, the first server maydetermine, based on the user question in the interaction text, a servicerequested by the user, and further determine the service complexitybased on a service type and a preset correspondence between the servicetype and the service complexity. A more complex service corresponds tohigher service complexity. For the user complexity, the first server maydetermine the user complexity based on a quantity of user questions inthe interaction text. More user questions correspond to higher usercomplexity.

Certainly, there are other manners of determining the AI complexity, theservice complexity, and the user complexity. In a possibleimplementation, the AI complexity is determined based on at least one ofa quantity of question repetitions, a quantity of recognition failures,a questioning keyword, a user tone change, a maximum duration ofquestion recognition, or a maximum length of a single reply. The servicecomplexity is determined based on at least one of a user consultationduration, a quantity of rounds of consultation interaction, or a servicelevel. The user complexity is determined based on a quantity of times ofrepeated dialing for a question.

The quantity of question repetitions is a quantity of times of repeatinga user question or a quantity of times of repeating a statement. Thefirst server may determine, by retrieving a plurality of rounds of voicerecognition results of user questions, whether there is a same questionor whether a statement is repeated, and use a quantity of same questionsor a quantity of times of repeating a statement as the quantity ofquestion repetitions. Considering that the intelligent voice server isconfigured to recognize a user question to obtain a voice recognitionresult, the quantity of question repetitions may be obtained by theintelligent voice server and then returned to the first server. Thequantity of recognition failures is a quantity of times that the AIrobot cannot understand a voice recognition result of a user question.The AI robot performs semantic understanding on the voice recognitionresult to obtain a semantic understanding result. If the AI robot cannotunderstand the voice recognition result, the semantic understandingresult includes preset description information. The first server maysearch for the semantic understanding result of the AI robot, and use aquantity of times that the preset description information appears as thequantity of recognition failures. For example, the preset descriptioninformation may be “Fail to understand your problem”. Considering thatthe NLP module of the AI robot is configured to perform semanticunderstanding on the voice recognition result of the user question, thequantity of recognition failures may be returned by the NLP module tothe first server when the NLP module returns the semantic understandingresult. The questioning keyword is a word that represents a doubt in auser question. The first server may retrieve a plurality of rounds ofvoice recognition results of the user, determine whether there is aquestioning keyword and whether there is a same questioning keyword, andcollect statistics on a quantity of questioning keywords or a quantityof same questioning keywords. Similar to the quantity of questionrepetitions, the questioning keyword may be obtained by the intelligentvoice server and then returned to the first server. The user tone(emotion) change is a tone change of the user in a process ofcommunicating with the AI robot. The intelligent voice server checks thetone change of the user, and returns the tone change to the first serveras a voice recognition result. The first server may convert the usertone change into a number. The maximum duration of question recognitionis a maximum duration consumed for recognizing a complete user problem.After a plurality of rounds of interaction between the AI robot and theuser, the first server may learn of, from the interaction text, aduration consumed by the user to ask each problem and obtain arecognition result of the problem, and obtain a maximum duration as themaximum duration of question recognition. The maximum length of a singlereply is a maximum length of a retrieval result (namely, a reply to aquestion) of the AI robot for a user question. The length may be aquantity of words, lines, or pages. After a plurality of rounds ofinteraction between the AI robot and the user, the first server maylearn of, from the interaction text, a quantity of words, lines, orpages of a reply to each user question, and obtain a maximum quantity ofwords or lines as the maximum length of a single reply. For obtaining ofthe AI complexity, the first server may perform weighted summation onthe quantity of question repetitions, the quantity of recognitionfailures, the questioning keyword, the user tone change, the maximumduration of question recognition, and the maximum length of a singlereply in a digital form, to obtain the AI complexity in a digital form.

The user consultation duration is an average consultation duration ofusers, and a consultation duration is a duration from a start ofinteraction between a user and the AI robot to an end of the interactionwith the AI robot. The first server may obtain an average processingduration of a corresponding service by associating a database. Forexample, a call record of a user who requests handling of each serviceis recorded in the database, and a user call record of a current servicemay be selected through screening, including a time point at which aconnection for a call is successfully established and a time point atwhich the call ends. Duration of each user call record may be obtainedbased on the two time points, and then an average value of durations ofuser calls is used as the user consultation duration. The quantity ofrounds of consultation interaction is an average value of quantities ofrounds of interaction between users and the AI robot. The first servermay obtain an average value of quantities of rounds of interaction for acorresponding service by associating the database. For example, the usercall record selected through screening further includes a quantity oftimes of interaction between a user and the AI robot, and the firstserver may use an average value of quantities of times of interactionbetween users and the AI robot as the quantity of rounds of consultationinteraction. Service levels are defined for different types of services,such as a level 1, a level 2, and . . . . For obtaining of the servicecomplexity, the first server may perform a weighted summation on theuser consultation duration, the quantity of rounds of consultationinteraction, and the service level in a digital form, to obtain theservice complexity in a digital form.

The quantity of times of repeated dialing for a question is a quantityof times of repeated service handling. The first server may retrieve aservice in the interaction text, determine whether the service isrepeatedly handled, and determine a quantity of times of repeatedhandling. The quantity of times is used as the quantity of times ofrepeated dialing for a question. For obtaining of the user complexity,the first server may directly obtain the quantity of times of repeateddialing for a question in a digital form as the user complexity, ormultiply the quantity of times of repeated dialing for a question by apreset coefficient to obtain the user complexity in a digital form.

The service level value of the AI robot for the call is determined byusing the at least one of the AI complexity, the service complexity, orthe user complexity. Because impact of factors such as the AI robot, theservice, and the user are considered, accuracy of the determined servicelevel value is relatively high.

405B. Obtain the service level value based on the at least one of the AIcomplexity, the service complexity, or the user complexity.

In a possible implementation, the first server may perform weightedsummation on the at least one of the AI complexity, the servicecomplexity, or the user complexity, and use a result of the weightedsummation as the service level value. Weights of the AI complexity, theservice complexity, and the user complexity may be configured based onoperating experience. For example, AI service level=AIcomplexity×S1+Service complexity×S2+User complexity×S3. S1, S2, and S3are weights. For example, S1 is 0.3, S2 is 0.3, and S3 is 0.4.

It should be noted that step 405 may be performed by the monitoring anddetection module of the first server.

406. When the service level value meets a first preset condition, thefirst server sends alarm information to the target agent device, wherethe target agent device is a device of a human agent that assists the AIrobot in call processing.

The alarm information is used to instruct the target agent device toperform an intervention operation on the call, to assist the AI robot inprocessing the call. As shown by a procedure indication line 3 in FIG.5, the first server may interact with an agent device by using thefourth server, for example, send the alarm information to the agentdevice. For example, the alarm information may include call informationthat is used to indicate a call that requires intervention of the targetagent device. In a possible implementation, the first preset conditionmay be set based on a service, and different services correspond todifferent first preset conditions. For example, different servicescorrespond to different expected service evaluation values or expectedservice level values.

In a possible implementation, a process in which the first serverdetermines that the service level value meets the first preset conditionmay include:

obtaining an estimated service evaluation value based on the servicelevel value, a historical service level value, and a historical serviceevaluation value, for example, Estimated service evaluationvalue=(Historical service evaluation value/Historical service levelvalue)×Service level value; and when the estimated service evaluationvalue is less than the historical service evaluation value or a presetexpected service evaluation value, determining that the service levelvalue meets the first preset condition.

In an embodiment, the historical service level value may be a servicelevel value of any previous call of a same service. For example, whenthe AI robot processes any previous call of a same service, the systemcalculates an evaluation value of a service level of the call. Thehistorical service evaluation value is a satisfaction evaluation valueof any previous call of a same service. For example, when the AI robotprocesses any previous call of a same service, the user feeds back asatisfaction evaluation on call processing, and the satisfactionevaluation may be converted into a number. For example, if thesatisfaction evaluation is five stars or very satisfied, the historicalservice evaluation value is 5. The expected service evaluation value isan expected value of a user evaluation.

In this manner, an alarm mechanism for a service level is determinedaccording to a policy for ensuring a user service evaluation. Ahistorical service evaluation is used to reflect whether the user issatisfied with the AI robot when the AI robot processes the call at ahistorical service level and a degree of satisfaction. Therefore, aservice evaluation obtained when the AI robot processes the call at acurrent service level may be estimated based on the historical servicelevel and the historical service evaluation. In this way, an alarm forthe service level can meet a real intention of the user to some extent.

FIG. 7 is a schematic diagram of an alarm scenario based on a serviceevaluation value according to an embodiment of this disclosure. As shownin FIG. 7, in scenarios A and D, if an estimated service evaluationvalue is greater than an expected service evaluation value and ahistorical service evaluation value, no alarm is triggered. In each ofscenarios B, C, E, and F, if the estimated service evaluation value isless than an expected service evaluation value or a historical serviceevaluation value, an alarm is triggered.

In addition, the first server may further obtain a ranking position ofeach call based on a service level value of the AI robot for each call,then perform ranking based on the ranking position, and instruct, bysending alarm information, an agent device for intervention. A callranked higher is preferentially intervened in. For example, the firstserver may obtain a ranking value of each call based on a first presetalgorithm. The ranking value is used to determine a ranking position,and a larger ranking value indicates a call ranked higher.

The first preset algorithm may be: Ranking value=(Expected serviceevaluation value−Estimated service evaluation value)×S4+(Historicalservice evaluation value−Estimated service evaluation value)×S5. S4 andS5 are weights used to calculate the ranking value and may be configuredbased on operating experience. For example, S4 is 0.5, and S5 is 0.5.

In a possible implementation, a process in which the first serverdetermines that the current service level value meets the first presetcondition may include: determining an overall service level value; andwhen the service level value is less than the overall service levelvalue or a preset expected service level value, determining that theservice level value meets the first preset condition. The overallservice level value is an average value of service level values of allcalls of the AI robot for a same service (namely, a current callingservice) in a preset period. For example, for each call of the service,the system may obtain a service level value of the AI robot for eachcall in a specific sampling period, and then use an average value ofservice level values obtained in a plurality of sampling periods as theoverall service level value. The plurality of sampling periods are thepreset period.

In this manner, an alarm mechanism for a service level is determinedaccording to a policy for ensuring an overall service level. The overallservice level value can reflect an average service level of processingcalls of a same service by the AI robot. Therefore, an alarm for theservice level can improve alarm accuracy to some extent in this manner.

When the current service level value is greater than the expectedservice level value and the overall service level value, no alarm istriggered. When the service level value is less than the expectedservice level value or the overall service level value, an alarm istriggered. A ranking value of each call is obtained based on a secondpreset algorithm to determine a ranking position of each call, and alarminformation is sent to instruct an agent device for intervention. A callranked higher is preferentially intervened in.

The second preset algorithm may be: Ranking value=(Expected servicelevel value−Current service level value)×S5+(Overall service levelvalue−Current service level value)×S6. S5 and S6 are weights used tocalculate the ranking value and may be configured based on operatingexperience. For example, S5 is 0.5, and S6 is 0.5. FIG. 8 is a schematicdiagram of monitoring and evaluating a call according to an embodimentof this disclosure. As shown in FIG. 8, a monitoring and detectionmodule of a first server evaluates a service level of an AI robot, andwhen a service level value meets a preset condition, an alarm isautomatically triggered, so that a human agent can pay attention to orintervene in a call processing process of the AI robot in advance. Inthis way, a user question and a reply are corrected in real time. In ascenario in which the AI robot cannot process a call, a problem that ittakes an excessively long time for the user to proactively transfer thecall to the human agent and for the human agent to directly undertake anAI call is resolved, thereby ensuring an overall service level of asystem and user satisfaction.

407. When receiving the alarm information, the target agent deviceperforms an intervention operation on the call.

In this embodiment of this disclosure, the intervention operationperformed by the target agent device on the call may be an interventionoperation indicated by a silence rule that is set when a service issubscribed to. The intervention operation may include text correction,call interception, and/or interposition. Correspondingly, that thetarget agent device performs an intervention operation on the callincludes: the interaction text is corrected by using the target agentdevice; the call is transferred to the target agent device forprocessing; and/or a three-party conference connection is establishedamong the target agent device, the AI robot, and the user equipment.

If the intervention operation is text correction (namely,preprocessing), the target agent device monitors interaction between theAI robot and the user in a text format. During the intervention, theuser question and the reply are also corrected in a text format, withoutindependently occupying the entire target agent device. Therefore, thetarget agent device may simultaneously monitor interaction texts of aplurality of calls and perform an intervention operation on theplurality of calls.

If the intervention operation is call interception, after the callinitiated by the user is transferred to the target agent device, thetarget agent device may be fully responsible for processing a subsequentservice of the user. This ensures that when the AI robot cannot processa user service, the user service can be processed by the human agent ina timely manner.

Usually, for a complex service, if the intervention operation isinterposition (also referred to as call pickup), the human agent mayform a three-party conference with the user and the AI robot, and thehuman agent can hear interaction between the user and the AI robot. Ifthe AI robot cannot answer a question in the interaction process, thehuman agent may directly answer the question of the user in place of theAI robot, thereby improving interaction efficiency.

In a possible implementation, that the target agent device corrects theinteraction text includes: sending the interaction text to the targetagent device; obtaining a corrected question text sent by the targetagent device, where the corrected question text is a text obtained aftera voice recognition result of the user question is corrected; obtainingthe reply to the user question based on the corrected question text;obtaining a corrected reply text sent by the target agent device, wherethe corrected reply text is a text obtained after the reply to the userquestion is corrected; and playing the corrected reply text.

For example, when the first server synchronously pushes the recognitionresult of the user question to the target agent device, a target agentmay correct the recognition result of the user question, and then submita corrected question text to the first server on the target agentdevice. The third server may obtain the corrected question text from thefirst server, and send the corrected question text to the AI robot. TheAI robot performs analysis processing on the corrected question text byusing the NLP module, including retrieving the reply to the userquestion, generating a reply in a form of a natural language text, andthen returning the reply to the third server. Then, the third serversends the reply to the first server. In this case, the first server maysynchronously push the reply to the target agent device, and aftercorrecting the reply, the target agent may submit the corrected replytext to the first server on the target agent device. Then, the firstserver requests the second server to play the corrected reply text, andthe second server requests the intelligent voice server to perform voicesynthesis on the reply, for example, a TTS module of the intelligentvoice server performs voice synthesis, and then plays the synthesizedvoice to the user.

It should be noted that this embodiment is described by using only anexample in which the target agent device corrects the recognition resultof the user question and the reply to the user question. In otherembodiments, the target agent device may further correct a semanticunderstanding result of the user question, so that the AI robot canretrieve the reply based on a corrected semantic understanding result,thereby further ensuring accuracy of the reply. In this case, theinteraction text pushed by the first server to the target agent devicefurther includes the semantic understanding result of the AI robot forthe user question, and the semantic understanding result is a resultobtained after the AI robot performs semantic understanding on therecognition result of the user question.

In a possible implementation, after the interaction text is corrected byusing the target agent device, the target agent device may stop theintervention operation at an appropriate occasion. For example, at afirst quitting occasion, when the service level value of the AI robotfor the call meets a second preset condition, the intervention operationon the call is stopped. At a second quitting occasion, after the voicerecognition result of the user question is corrected, the interventionoperation on the call is stopped. The intervention on the call may bequit at an appropriate occasion, thereby avoiding the problem thatworking efficiency is affected because the human agent is occupied foran excessively long time.

The second preset condition may be that the service level value of theAI robot for the call is greater than the expected service level valueand the overall service level value. Alternatively, the second presetcondition may be another condition. This is not limited in embodimentsof this disclosure. The second quitting occasion may be that theintervention is quit after an interference problem is resolved. Theinterference problem is a user problem that cannot be understood by theAI robot.

It should be noted that step 406 and step 407 are in a possibleimplementation in which the target agent device performs theintervention operation on the call when the service level value meetsthe first preset condition. When the service level value of the AI robotfor the call does not meet the condition, alarm information is sent tothe target agent device, so that the target agent device can intervenein the call based on the alarm information in a timely manner.Alternatively, when the service level value meets the first presetcondition, the first server may directly perform the interventionoperation on the call by using the target agent device, without sendingthe alarm information to the target agent device. For example, the firstserver instructs, by sending an intervention instruction to the targetagent device, the target agent device to intervene in the call.

FIG. 9 is a diagram illustrating a call processing process according toan embodiment of this disclosure. As shown by a procedure indicationline 1 in FIG. 9, an administrator configures a silence rule by using asilence rule configuration module of a first server (corresponding tostep 401). As shown by a procedure indication line 2 in FIG. 9, an agentdevice logs in to a system by using a fourth server and subscribes to acall of a corresponding service by using a subscription managementmodule of the first server (corresponding to step 402). As shown by aprocedure indication line 3 in FIG. 9, when a user makes a call to thesystem, the system routes the user call to the agent device(corresponding to step 403). As shown by a procedure indication line 4in FIG. 9, a monitoring and detection module of the first servermonitors a call processing process of an AI robot, and evaluates aservice level of the AI robot (corresponding to step 404 and step 405).As shown by a procedure indication line 5 in FIG. 9, the agent deviceintervenes in the call (corresponding to step 406 and step 407).

In the technical solution provided in this embodiment of thisdisclosure, the first server implements silence rule customization, callmonitoring and subscription, call processing process monitoring,automatic detection, and real-time intervention. An agent devicemonitors, based on a service capability of the agent device, a pluralityof calls that are being processed by the AI robot. The agent device maysimultaneously browse interaction content of the calls. The systemautomatically detects the interaction content and evaluates a servicelevel, and issues an alarm for a call with a relatively low evaluationvalue, to trigger the agent device to perform intervention such as textcorrection, interception, and interposition in real time.

For the user, after several rounds of interaction with the AI robot, thesystem automatically understands and continues to process a subsequentservice of the user. In this way, the user does not perceive theintervention of the human agent, so that service experience of the useris good. For the human agent, the human agent only corrects a recognizedproblem or a retrieved answer, and subsequent service handling is stillcompleted by the AI robot. The human agent only participates in theassistance to the AI robot in some phases of the service procedure, sothat working efficiency is relatively high. For the system, the userstrives to interact with the AI robot and finally completes servicehandling. The user is aware of intelligence and learning capability ofthe system, thereby enhancing approval from the user for the system. Inaddition, the human agent only assists the AI robot in work, and mayfurther correct and train the system more efficiently andprofessionally.

Currently, in some professional and complex language scenarios,interaction between the user and the AI robot cannot reach a servicelevel of a common human agent. The foregoing solution in which humanassistance to the AI robot is used to process a call is an efficient andlow-cost solution, to improve service quality of the AI robot and ensurethat the AI robot can process the call without a blind spot, therebyimproving overall satisfaction of the system. It should be noted thatembodiments of this disclosure are described by using an example inwhich the human agent assists the AI robot. Alternatively, the foregoingtechnical solution is also applicable to a human assistance scenario ofanother kind of intelligent device.

According to the method provided in this embodiment of this disclosure,the service level of the AI robot is evaluated, and when the servicelevel value meets the preset condition, the human agent is automaticallytriggered for intervention, and the human agent only assists in the callprocessing process of the AI robot. In this way, a problem that it takesan excessively long time for the human agent to directly undertake acall is resolved, so that working efficiency of the human agent and anoverall service level of the system are ensured, thereby improving callprocessing efficiency.

FIG. 10 is a schematic diagram of a call processing apparatus accordingto an embodiment of this disclosure. Referring to FIG. 10, the apparatusincludes a monitoring and detection module 1001 and an interventionoperation module 1002.

The monitoring and detection module 1001 is configured to monitor a callprocessing process of an artificial intelligence AI robot, to obtain aninteraction text of the call, where the interaction text includes arecognition result of a user question and a reply to the user question.

The monitoring and detection module 1001 is configured to obtain aservice level value of the AI robot for the call based on theinteraction text.

The intervention operation module 1002 is configured to: when theservice level value meets a first preset condition, perform anintervention operation on the call by using a target agent device, wherethe target agent device is a device of a human agent that assists the AIrobot in call processing.

In a possible implementation, the monitoring and detection module 1001is configured to perform the process of obtaining the service levelvalue in the step 405.

In a possible implementation, the AI complexity is determined based onat least one of a quantity of question repetitions, a quantity ofrecognition failures, a questioning keyword, a user tone change, amaximum duration of question recognition, or a maximum length of asingle reply.

The service complexity is determined based on at least one of a userconsultation duration, a quantity of rounds of consultation interaction,or a service level.

The user complexity is determined based on a quantity of times ofrepeated dialing for a question.

In a possible implementation, the monitoring and detection module 1001is further configured to perform the process of determining, in the step406, that the service level value meets the first preset condition.

In a possible implementation, the intervention operation module 1002 isconfigured to perform the process of performing an interventionoperation on the call in the step 407.

In a possible implementation, the intervention operation module 1001 isfurther configured to perform the process of stopping the interventionoperation on the call in the step 407.

In a possible implementation, the monitoring and detection module 1001is further configured to perform the process of determining the targetagent device in the step 403.

In a possible implementation, the monitoring and detection module 1001is further configured to perform the process of adding the call to themonitoring queue of the target agent device in the step 403.

In a possible implementation, the first preset condition is set based ona service type.

According to the apparatus provided in this embodiment of the presentdisclosure, the service level of the AI robot is evaluated, and when theservice level value meets a preset condition, the human agent isautomatically triggered for intervention, and the human agent onlyassists in the call processing process of the AI robot. In this way, aproblem that it takes an excessively long time for the human agent todirectly undertake a call is resolved, so that working efficiency of thehuman agent and an overall service level of a system are ensured,thereby improving call processing efficiency.

An embodiment of this disclosure further provides a call processingsystem. The system includes an AI robot, a first server, and a targetagent device.

The AI robot is configured to process a call. The first server isconfigured to monitor, in real time, a call processing process of the AIrobot, to obtain an interaction text of the call, where the interactiontext includes a recognition result of a user question and a reply to theuser question. The first server is further configured to obtain aservice level value of the AI robot for the call based on theinteraction text. The target agent device is configured to perform anintervention operation on the call when the service level value meets afirst preset condition, where the target agent device is a device of ahuman agent that assists the AI robot in call processing.

In a possible implementation, the first server is configured to performthe process of obtaining the service level value in the step 405.

In a possible implementation, the AI complexity is determined based onat least one of a quantity of question repetitions, a quantity ofrecognition failures, a questioning keyword, a user tone change, amaximum duration of question recognition, or a maximum length of asingle reply.

The service complexity is determined based on at least one of a userconsultation duration, a quantity of rounds of consultation interaction,or a service level.

The user complexity is determined based on a quantity of times ofrepeated dialing for a question.

In a possible implementation, the first server is further configured toperform the process of determining, in the step 406, that the servicelevel value meets the first preset condition.

In a possible implementation, the target agent device is configured toperform the process of performing an intervention operation on the callin the step 407.

In a possible implementation, the system further includes a third serverfor interactive voice response and a second server for unified sessionmanagement.

The first server is further configured to perform the process of sendingthe interaction text to the target agent device in the step 404. Thetarget agent device is configured to perform the process of sending thecorrected question text in the step 407. The third server is configuredto perform the process of obtaining the corrected question text andsending the corrected question text to the AI robot in the step 407, andthe AI robot is further configured to perform the process of obtaining areply in the step 407. The third server is configured to perform theprocess of obtaining the corrected reply text in the step 407. Thesecond server is configured to perform the process of playing thecorrected reply text in the step 407.

In a possible implementation, the target agent device is furtherconfigured to perform the process of stopping the intervention operationon the call in the step 407.

In a possible implementation, the first server is further configured toperform the process of determining the target agent device in the step403.

In a possible implementation, the first server is further configured toperform the process of adding the call to the monitoring queue of thetarget agent device in the step 403.

In a possible implementation, the first preset condition is set based ona service type.

According to the system provided in embodiments of the presentdisclosure, the service level of an AI robot is evaluated, and when theservice level value meets a preset condition, a human agent isautomatically triggered for intervention, and the human agent onlyassists in the call processing process of the AI robot. In this way, aproblem that it takes an excessively long time for the human agent todirectly undertake a call is resolved, so that working efficiency of thehuman agent and an overall service level of a system are ensured,thereby improving call processing efficiency.

A person of ordinary skill in the art may understand that all or some ofthe steps of the embodiments may be implemented by hardware or a programinstructing related hardware. The program may be stored in acomputer-readable storage medium. The storage medium may include: aread-only memory, a magnetic disk, or an optical disc.

The foregoing descriptions are merely alternative embodiments of thisdisclosure, but are not intended to limit this disclosure. Anymodification, equivalent replacement, or improvement made withoutdeparting from the spirit and principle of this disclosure should fallwithin the protection scope of this disclosure.

What is claimed is:
 1. A call processing method implemented by a server,comprising: monitoring, in real time, a call processing process of anartificial intelligence (AI) robot, to obtain an interaction text of acall, wherein the interaction text comprises a recognition result of auser question and a reply to the user question; obtaining a servicelevel value of the AI robot for the call based on the obtainedinteraction text, the service level value indicating service level ofthe AI robot; determining whether the service level value meets a firstcondition; and in response to the determination that the service levelvalue meets the first condition, performing an intervention operation onthe call by using a target agent device, wherein the target agent deviceis a device providing for a human agent to assist the AI robot in callprocessing, wherein the method further comprises: obtaining an estimatedservice evaluation value based on the service level value, a historicalservice level value, and a historical service evaluation value; anddetermining that the service level value meets the first condition inresponse to the determination that the obtained estimated serviceevaluation value is less than the historical service estimation value.2. The method according to claim 1, wherein the obtaining a servicelevel value of the AI robot for the call based on the obtainedinteraction text comprises: determining at least one of AI complexity,service complexity, or user complexity based on the obtained interactiontext, wherein the AI complexity reflects service quality of the AIrobot, the service complexity reflects a complexity degree of a service,and the user complexity reflects a degree of user requirements forservice handling; and obtaining the service level value based on thedetermined at least one of the AI complexity, the service complexity, orthe user complexity.
 3. The method according to claim 2, wherein the AIcomplexity is determined based on at least one of a quantity of questionrepetitions, a quantity of recognition failures, a questioning keyword,a user tone change, a maximum duration of question recognition, or amaximum length of a single reply; the service complexity is determinedbased on at least one of a user consultation duration, a quantity ofrounds of consultation interaction, or a service level; and the usercomplexity is determined based on a quantity of times of repeateddialing for a question.
 4. The call processing method according to claim2, wherein obtaining the service level value based on the determined atleast one of the AI complexity, the service complexity, or the usercomplexity comprises performing weighted summation on the at least oneof the AI complexity, the service complexity, or the user complexity. 5.The call processing method according to claim 4, wherein performingweighted summation on the at least one of AI complexity, servicecomplexity, or user complexity comprises: configuring weights of the AIcomplexity, the service complexity, and the user complexity; andperforming the weighted summation using at least one of the weight ofthe AI complexity, the weight of the service complexity, or the weightof the user complexity.
 6. The method according to claim 1, wherein thehistorical service level value is a service level value of any previouscall of a same service, the historical service evaluation value is asatisfaction evaluation value of any previous call of a same service,and the method further comprises: determining whether the estimatedservice evaluation value is less than the historical service evaluationvalue or an expected service evaluation value; and determining that theservice level value meets the first condition in response to thedetermination that the estimated service evaluation value is less thanthe expected service evaluation value.
 7. The method according to claim1, further comprising: determining an overall service level value,wherein the overall service level value is an average value of servicelevel values of the AI robot for all calls of a same service in aperiod; determining whether the service level value is less than theoverall service level value or an expected service level value; anddetermining that the service level value meets the first condition inresponse to the determination that the service level value is less thanthe overall service level value or the expected service level value. 8.The method according to claim 1, wherein the performing an interventionoperation on the call by using a target agent device comprises: sending,by the server, the obtained interaction text to the target agent device,and obtaining a corrected question text and a corrected reply text thatare obtained after the target agent device corrects the interactiontext; or transferring the call to the target agent device forprocessing; or establishing a three-party conference connection amongthe target agent device, the AI robot, and user equipment that initiatesthe call.
 9. The method according to claim 8, further comprising, afterthe obtaining a corrected question text and a corrected reply text thatare obtained after the target agent device corrects the interactiontext: stopping the intervention operation on the call when the servicelevel value of the AI robot for the call meets a second condition; orstopping the intervention operation on the call after correcting a voicerecognition result of the user question.
 10. The method according toclaim 1, further comprising, before the performing an interventionoperation on the call by using a target agent device: determining thetarget agent device based on a service corresponding to the call andsubscription information, wherein the subscription information records aservice to which each agent device subscribes.
 11. The method accordingto claim 10, further comprising, after the determining the target agentdevice based on a service corresponding to the call and subscriptioninformation: adding the call to a monitoring queue of the target agentdevice, wherein the monitoring queue manages call information, a callmonitoring status, and an interaction text of at least one call, thecall information comprises a call number, calling party information, andcalled party information, and the call monitoring status comprises anidle state, a monitoring state, or an intervention state.
 12. The methodaccording to claim 1, wherein the first condition is set based on aservice type.
 13. A server, comprising at least one processor and amemory, the memory stores a computer program, that when executed by theat least one processor, causes the server to perform operationscomprising: monitoring, in real time, a call processing process of anartificial intelligence (AI) robot, to obtain an interaction text of acall, wherein the interaction text comprises a recognition result of auser question and a reply to the user question; obtaining, a servicelevel value of the AI robot for the call based on the obtainedinteraction text; determining whether the service level value meets afirst condition; and in response to the determination that the servicelevel value meets the first condition, performing an interventionoperation on the call by using a target agent device, wherein the targetagent device is a device providing for a human agent to assist the AIrobot in call processing, wherein the operations further comprise:obtaining an estimated service evaluation value based on the servicelevel value, a historical service level value, and a historical serviceevaluation value; and determining that the service level value meets thefirst condition in response to the determination that the obtainedestimated service evaluation value is less than the historical serviceestimation value.
 14. The server according to claim 13, wherein theoperations further comprise: determining at least one of AI complexity,service complexity, or user complexity based on the interaction text,wherein the AI complexity reflects service quality of the AI robot, theservice complexity reflects a complexity degree of a service, and theuser complexity reflects a degree of user requirements for servicehandling; and obtaining the service level value based on the determinedat least one of the AI complexity, the service complexity, or the usercomplexity.
 15. The server according to claim 14, wherein the AIcomplexity is determined based on at least one of a quantity of questionrepetitions, a quantity of recognition failures, a questioning keyword,a user tone change, a maximum duration of question recognition, or amaximum length of a single reply; the service complexity is determinedbased on at least one of a user consultation duration, a quantity ofrounds of consultation interaction, or a service level; and the usercomplexity is determined based on a quantity of times of repeateddialing for a question.
 16. The server according to claim 13, whereinthe historical service level value is a service level value of anyprevious call of a same service, the historical service evaluation valueis a satisfaction evaluation value of any previous call of a sameservice, and the operations further comprise: determining whether theestimated service evaluation value is less than the historical serviceevaluation value or an expected service evaluation value; anddetermining that the service level value meets the first condition inresponse to the determination that the estimated service evaluationvalue is less than the expected service evaluation value.
 17. The serveraccording to claim 13, wherein the operations further comprise:determining an overall service level value, wherein the overall servicelevel value is an average value of service level values of the AI robotfor all calls of a same service in a period; determining whether theservice level value is less than the overall service level value or anexpected service level value; and determining that the service levelvalue meets the first condition in response to the determination thatthe service level value is less than the overall service level value orthe expected service level value.
 18. The server according to claim 13,the operations further comprise: sending the interaction text to thetarget agent device, and obtain a corrected question text and acorrected reply text that are obtained after the target agent devicecorrects the interaction text; or transferring the call to the targetagent device for processing; or establishing a three-party conferenceconnection among the target agent device, the AI robot, and userequipment, wherein the user equipment is a device that initiates thecall.
 19. The server according to claim 18, the operations furthercomprise: stopping the intervention operation on the call when theservice level value of the AI robot for the call meets a secondcondition; or stopping the intervention operation on the call aftercorrecting the recognition result of the user question.
 20. The serveraccording to claim 13, the operations further comprise: determining thetarget agent device based on a service corresponding to the call andsubscription information, wherein the subscription information records aservice to which each agent device subscribes.
 21. The server accordingto claim 13, wherein the first condition is set based on a service type.22. A call processing system comprising an artificial intelligence (AI)robot, a server, and a target agent device, wherein the AI robot isconfigured to process a call, and wherein the server is configured to:monitor a call processing process of an AI robot in real time; obtain aninteraction text of the call, wherein the interaction text comprises arecognition result of a user question and a reply to the user question;obtain a service level value of the AI robot for the call based on theobtained interaction text, the service level value indicating servicelevel of the AI robot; determine whether the service level value meets afirst condition; and in response to the determination that the servicelevel value meets the first condition, perform an intervention operationon the call by using the target agent device, wherein the target agentdevice is a device providing for a human agent to assist the AI robot incall processing, wherein the server is further configured to: obtain anestimated service evaluation value based on the service level value, ahistorical service level value, and a historical service evaluationvalue; and determine that the service level value meets the firstcondition in response to the determination that the obtained estimatedservice evaluation value is less than the historical service evaluationvalue.